Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "155" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 19 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 18 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459832 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 30.102733 | 31.610276 | 24.548538 | 25.748616 | 13.830607 | 13.647028 | 3.139416 | 4.463874 | 0.0402 | 0.0393 | 0.0015 | 1.352444 | 1.341376 |
| 2459831 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.076267 | 1.911297 | 0.142607 | -0.248483 | 1.523899 | 6.870049 | 6.494329 | 9.436866 | 0.0330 | 0.0311 | -0.0000 | nan | nan |
| 2459830 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 30.781696 | 30.705296 | 35.095375 | 36.690241 | 39.851672 | 36.032312 | 6.442474 | 6.476014 | 0.0380 | 0.0353 | 0.0010 | 1.338522 | 1.329019 |
| 2459829 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 28.676620 | 30.549573 | 28.595871 | 29.969735 | 28.579406 | 31.866893 | 9.375689 | 8.853993 | 0.0391 | 0.0349 | 0.0016 | 1.227630 | 1.180914 |
| 2459828 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 25.887356 | 27.040748 | 30.897216 | 32.091207 | 36.606029 | 33.173499 | 14.624222 | 12.776758 | 0.0401 | 0.0387 | 0.0000 | 0.000000 | 0.000000 |
| 2459827 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 21.956040 | 21.497152 | 34.712234 | 36.544962 | 24.494588 | 26.035078 | 1.522957 | 1.342058 | 0.0354 | 0.0380 | 0.0009 | 1.205887 | 1.327700 |
| 2459826 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 23.679907 | 23.345044 | 38.999537 | 40.373196 | 48.961391 | 45.085352 | 8.477381 | 9.207630 | 0.0353 | 0.0364 | 0.0008 | 1.147432 | 1.316705 |
| 2459825 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 26.114823 | 25.658233 | 31.039055 | 32.273727 | 27.658898 | 25.814621 | 0.575619 | 0.636319 | 0.0381 | 0.0410 | 0.0004 | 1.296360 | 1.289411 |
| 2459824 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 14.218793 | 15.299322 | 24.654161 | 26.098103 | 10.672335 | 19.341977 | 3.234986 | 3.668929 | 0.0375 | 0.0395 | 0.0008 | 1.242325 | 1.291609 |
| 2459823 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 24.170496 | 23.948184 | 46.267877 | 47.786683 | 34.300356 | 35.729230 | 33.580352 | 34.317987 | 0.0345 | 0.0331 | 0.0011 | 1.361382 | 1.287465 |
| 2459822 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 25.289755 | 25.857331 | 42.455554 | 43.914117 | 30.794210 | 29.420539 | 1.415336 | 1.095268 | 0.0357 | 0.0362 | 0.0002 | 1.272979 | 1.267945 |
| 2459821 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 27.682211 | 28.621774 | 43.484652 | 44.621657 | 26.596251 | 26.166870 | -0.461388 | -0.153914 | 0.0341 | 0.0339 | 0.0004 | 1.220436 | 1.216489 |
| 2459820 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 20.691806 | 22.496275 | 34.139850 | 36.538904 | 65.077478 | 68.747804 | 26.241276 | 5.594456 | 0.0344 | 0.0321 | 0.0014 | 0.000000 | 0.000000 |
| 2459817 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 23.577894 | 25.167669 | 42.273794 | 43.428833 | 36.313217 | 36.370781 | 1.976659 | 1.960543 | 0.0365 | 0.0378 | 0.0004 | 1.175253 | 1.172151 |
| 2459816 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 17.624955 | 18.312062 | 42.290336 | 44.175817 | 46.698024 | 45.185457 | 9.447206 | 10.343320 | 0.0432 | 0.0379 | 0.0028 | 1.319615 | 1.307611 |
| 2459815 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 21.617334 | 22.381697 | 46.219540 | 47.700309 | 47.464703 | 47.607568 | 11.453277 | 13.797554 | 0.0439 | 0.0436 | 0.0011 | 1.306944 | 1.310199 |
| 2459814 | digital_maintenance | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Shape | 31.610276 | 30.102733 | 31.610276 | 24.548538 | 25.748616 | 13.830607 | 13.647028 | 3.139416 | 4.463874 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Temporal Discontinuties | 9.436866 | -0.076267 | 1.911297 | 0.142607 | -0.248483 | 1.523899 | 6.870049 | 6.494329 | 9.436866 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | ee Temporal Variability | 39.851672 | 30.781696 | 30.705296 | 35.095375 | 36.690241 | 39.851672 | 36.032312 | 6.442474 | 6.476014 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Temporal Variability | 31.866893 | 30.549573 | 28.676620 | 29.969735 | 28.595871 | 31.866893 | 28.579406 | 8.853993 | 9.375689 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | ee Temporal Variability | 36.606029 | 27.040748 | 25.887356 | 32.091207 | 30.897216 | 33.173499 | 36.606029 | 12.776758 | 14.624222 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Power | 36.544962 | 21.956040 | 21.497152 | 34.712234 | 36.544962 | 24.494588 | 26.035078 | 1.522957 | 1.342058 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | ee Temporal Variability | 48.961391 | 23.345044 | 23.679907 | 40.373196 | 38.999537 | 45.085352 | 48.961391 | 9.207630 | 8.477381 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Power | 32.273727 | 25.658233 | 26.114823 | 32.273727 | 31.039055 | 25.814621 | 27.658898 | 0.636319 | 0.575619 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Power | 26.098103 | 14.218793 | 15.299322 | 24.654161 | 26.098103 | 10.672335 | 19.341977 | 3.234986 | 3.668929 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Power | 47.786683 | 23.948184 | 24.170496 | 47.786683 | 46.267877 | 35.729230 | 34.300356 | 34.317987 | 33.580352 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Power | 43.914117 | 25.289755 | 25.857331 | 42.455554 | 43.914117 | 30.794210 | 29.420539 | 1.415336 | 1.095268 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Power | 44.621657 | 28.621774 | 27.682211 | 44.621657 | 43.484652 | 26.166870 | 26.596251 | -0.153914 | -0.461388 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Temporal Variability | 68.747804 | 20.691806 | 22.496275 | 34.139850 | 36.538904 | 65.077478 | 68.747804 | 26.241276 | 5.594456 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Power | 43.428833 | 23.577894 | 25.167669 | 42.273794 | 43.428833 | 36.313217 | 36.370781 | 1.976659 | 1.960543 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | ee Temporal Variability | 46.698024 | 18.312062 | 17.624955 | 44.175817 | 42.290336 | 45.185457 | 46.698024 | 10.343320 | 9.447206 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Power | 47.700309 | 22.381697 | 21.617334 | 47.700309 | 46.219540 | 47.607568 | 47.464703 | 13.797554 | 11.453277 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 155 | N12 | digital_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |